Trends in Web-Based Education towards Adaptivity Katerina Georgouli Department of Informatics, TEI of Athens Egaleo, Greece
Abstract— In this paper we present the different categories of existing virtual learning environments and the recent research efforts towards adaptivity in that technological area. As Web-Based Education influences to a large extend the educational processes and e-learning becomes a common place in all educational environments the educational technology offers a considerable number of virtual learning environments to facilitate the learning process. These environments can be synchronous or asynchronous, show adaptive characteristics or not and be intelligent or not. Adaptivity has become a prominent research topic during the past decades offering a big number of adaptive web-based educational environments. Recent research focuses on the integration of these systems with computational intelligence technologies as well as on the integration of widely used open source learning management platforms with adaptive tools and adaptive web-services. Index Terms—Adaptive and intelligent web-based educational systems, e-learning, virtual learning environments, web-based education
I. INTRODUCTION With the rapid technological development in recent years, the abundance of computers and the prominence of the Internet, the influence of technology in education has led to the development of what is known as "Web-Based Education" (WBE). WBE is a type of online education which encompasses both the idea of courses which are accessible using the tools of the Internet as well as courses conducted entirely on the Internet (online). In general, online education is any education process offered through the use of electronic devices. Online education is firstly characterized by the physical separation of teachers and learners which distinguishes it from usual face-to-face education. Although students are physically scattered, online education bears the influence and perspective of an educational organization, in contrast to independent selfstudy or private tutoring. Also, the use of a computer network (of Internet in the case of WBE) to present or distribute educational content and the provision of multiway communication via the network allows students to benefit from online collaborative activities and online and offline discussions with one another and with their teachers and staff at their own pace and according to their interests and time [1]. It is a fact that WBE has already largely influenced the educational processes of e-learning. E-learning covers a wide set of applications and processes, including Web-
based learning, Computer-based learning, Virtual classrooms, and Digital collaboration. Commonly, elearning uses Internet technologies and can be divided into two main categories: synchronous and asynchronous elearning. Usually, higher educational courses are combining both of them into a single learning experience called “Blended Learning”. Both e-learning categories take advantage of synchronous communication tools, virtual group discussions and live questions-answers environments, indispensible tools in any e-learning system supporting WBE. The multimedia capabilities offered by computers in conjunction with the information and communication technologies make web-based educational systems essential and efficient tools to cover a wide range of educational processes. Furthermore, supported by artificial intelligence technologies, they can provide an interactive learning environment that allows for an adaptation to the student's needs and aptitudes. According to Franklin and Peat [2], the technological changes which bring new ideas/processes and which in parallel are considered superior to the previously used methods because of the variety of options they offer, yield long term innovations in society, one of which is WBE, either it adapts to users or not. II. VIRTUAL LEARNING ENVIRONMENTS E-learning systems have been widely adopted by educational institutions and organizations in recent years, in an attempt to enhance their traditional learning and training methodologies by using virtual learning environments (VLE). VLE are software systems that facilitate the processes of e-learning for groups and individuals. They are web-based and provide many managerial functions such as uploading, downloading and management of educational material, monitoring and evaluating of the students’ learning progress and they can integrate social software as wikis, blogs, RSS or other personal or public learning environments. A considerable number of VLE rely on the integration of external tools in the form of plug-ins or web services to become more effective and efficient to improve learners’ knowledge. A VLE must meet some minimum standards and offer to the instructors those tools that will help them design their courses remaining attractive to the students and easy to explore so that it can provide a rewarding learning experience. Support of learning technology standards like SCORM (Sharable Content Object Reference Model) and
IMS specifications and standards are indispensable in modern VLE. The term VLE, in spite of constant changes and revisions in e-learning that have created a confusion in the terminology, is a general term that comprises several types of systems that have been described as Learning Management Systems (LMS), Course Management Systems (CrMS), Learning Content Management Systems (LCMS), Managed Learning Environments (MLE), Learning Support Systems (LSS), Online Learning Centres (OLC) and Learning Platforms (LP). The main representatives of the above are LMS, CrMS and LCMS. Specifically, LMS focus mainly on the learner but also offer to the course manager all the tools needed for the design, delivery and management of the course (classroom or online), as well as supervision of the students’ progress throughout the educational process, by keeping the relevant statistical information. These systems also handle administrative tasks such as presenting usage information to the course manager and the system administrator. CrMS concentrate on the instructor and pay special attention to the educational content. They provide a set of tools which enable the course manager to create training materials, learning paths as well as the immediate content management during the training period. LCMS are mainly designed for creating, editing, revising, typesetting, searching, publishing and storing content files of any type. Currently, LMS and LCMS are more prevailing even though CrMS offer additional features and tools for the management of educational materials and learning paths. One of the main reasons for the lack of popularity of CrMS seems to be that most of them are commercial products whereas many of the most well-known LMS and LCMS are based on free open source software. Some of the most widely known such syste free open source LMS are Moodle 1 , Ilias 2 , Claroline 3 , Dokeos 4 and ATutor 5 . Examples of commercial software are JoomlaLMS 6 , Blackboard7 and WebCT8. Although adopting VLE has been widely accepted by educational institutions, mostly as auxiliary tools as explained above, there still exist some obstacles. For example, it is difficult to evaluate the performance of students, since only statistical data can be provided. Meanwhile, changes in learner behavior during the learning process induced by the predefined learning materials cannot catch up with the needs of each individual learner as VLE cannot react properly when difficulties occur, as human teachers do. Indeed, after an intensive experimentation during last two decades, researchers and 1
http://moodle.org/ http://www.ilias.de 3 http://www.claroline.net/ 4 http://www.dokeos.com 55 http://www.atutor.ca/ 6 http://www.joomlalms.com/ 7 http://www.blackboard.com/ 8 http://www.wbtsystems.com/ 2
practitioners have concluded that a VLE cannot replace the direct interaction between student and teacher. One of the main reasons, as already mentioned, is that the instructional steps followed in VLE are usually prescribed for all learners. As a consequence, they may not be able to meet the needs of each individual student as an autonomous entity which teacher always takes under consideration. Those needs are shaped by different particular skills, knowledge, objectives and interests. This leads to passive behavior of both teachers and students participating in e-learning activities and the VLE is only used as an auxiliary educational tool, as none of its users trusts it enough to allow it to become a key part of the educational process [3]. On the other hand, building personalized e-learning systems requires a large amount of time, money, and human resources. Therefore, it can be more appropriate and more effective to improve the existing e-learning systems in order to maximize the learning result, while avoiding the excessive cost of building a new fully personalized system. The researchers, trying to improve the VLE behaviour, have proposed a series of innovative practical technologies thus creating the foundations for a new generation of VLE having abilities of real interactivity and participation in the learning process. Such technologies are the adaptive and intelligent web-based educational systems (AIWBES) [4]. The most attractive characteristic of AIWBES is that they can provide a flexible learning path to the students, according to their skills and learning abilities. In such environments, students can decide where and when to learn which learning content according to their own needs and whims. III.
ADAPTIVE AND INTELLIGENT WEB-BASED EDUCATIONAL SYSTEMS
According to Brusilovsky and Peylo [4], AIWBES provide an alternative to the traditional approach of “justput-it-on-the-web” used in many e-learning approaches. In this context, the term “adaptive” refers to the modeling of knowledge level, preferences and attitudes of each student. The resulting model is used for setting personalized learning objectives and for designing the system’s interaction with the students in order to fit learning to their individual needs. Technically, adaptation is based on intelligent integration and implementation of procedures which are responsible for the educational processes that support the learning progress of the students. Examples of such processes are coaching students or diagnosing their misconceptions using the following main categories of techniques: Adaptive hypermedia, Adaptive information filtering, Intelligent (class) monitoring, Intelligent collaborative learning and Intelligent tutoring [4]. With the help of the consolidation of Internet and of educational technology, AIWBES have been expanding decisively, both in the student's modeling level (increasing the range of student characteristics which are taken into account) and on the usage of techniques of Computational Artificial Intelligence (incorporating more and more
sophisticated “intelligent” techniques) [5],[6]. Depending on which of the above mentioned tendencies the educational systems focus, we can classify them as “Adaptive Learning Environments” (ALE) or “Intelligent Educational Systems” (IES). ALE are tailored to each student or group of students, by taking into account information received from the student model. IES apply Artificial Intelligence techniques aiming at the highest levels of intelligent support for their users. The most widely known ALE are NetCoach [7], AHA! [8], InterBook [9], and APeLS [10]. Systems that are “purely” intelligent and fall in the IES class are German Tutor [11] and the SQL-Tutor [12]. Generally speaking the majority of the AIWES focus either in intelligence or in adaptability, and only a small number of them include both approaches. However, in order to enable a meaningful transformation in the delivery of education and training, AIWBES must become accessible and affordable using modern Web and AI technologies. They must also adapt to the needs of end users in more comprehensive ways. Nevertheless, when scaling to satisfy the needs of an everlarger and more diverse population of learners, these systems must remain simple and reliable and they must not indulge in increasingly complex interactive instructional activities. Examples of systems with such characteristics are ELM-ART [13] and WESLA [14]. IV. WEB AND AI TECHNOLOGIES TO ACHIEVE PERSONALIZATION IN LARGE SCALE E-LEARNING SYSTEMS Most of recent e-learning systems have departed the client/server architecture previously used and are now being designed as Distributed Learning Environments (DLE), with a large number of users. If fact, as the number of distributed learners increases, serious efficiency problems in e-learning courses development and maintenance start occurring due to the lack of personalization facilities. A number of Web and AI technologies such as web services, intelligent agents, semantic web and AI techniques can be used to overcome that situation, either as standalone technologies or as a hybrid approach emerging from the combination of different technologies [15]. Web services are applications that are designed to be independent as much as possible from specific platforms and which other applications can automatically discover and invoke. They can also easily be combined with other web-services, providing valuable functionality. Nevertheless, they have a number of limitations: webservices don’t know about their users or clients, they are not able to use ontologies, they stay passive until invoked and they cannot cooperate with one another. These limitations limit their value in supporting the personalization of e-learning needs. The Semantic web, a recent Web community effort, aims at adding semantic information to web contents in order to create an environment in which ALE could offer effective adaptive learning and intelligent agents could offer adaptive support efficiently. Semantic annotations added on course materials permit efficient reuse of those
materials in other courses and facilitate the search of preferred contents. A well-described knowledge, by the use of ontologies, is a key issue for designing suitable elearning platforms. This knowledge has to be expressed in a precise, machine-interpretable form and enables the interoperable application components to process domain data both on the syntactic and semantic level [16]. The Semantic web is also a promising technology for improving semantic interoperability of educational content in the form of Learning Objects (LO). AI techniques, like artificial neural networks, probabilistic reasoning, fuzzy logic, expert systems, evolutionary systems or combinations of them, are frequently used by educational technology. Those that are most often met are techniques supporting intelligence in Multi-Agent based DLE. Intelligent agents overcome the limitations of webservices, by having their own local knowledge and by fitting well in a Semantic web environment. Because of their autonomous nature, they can independently carry out tasks delegated to them and can act as both the requester and the provider of web-services. Agent techniques and technologies enhance the performance, the flexibility, the modularity and the effectiveness of several aspects of elearning systems. Intelligent agents can be used as the core components of intelligent DLE because they encompass the needed characteristics in such environments: autonomy, intelligence, socialization, etc. Intelligent agents can provide learning functions that offer personalized services with capabilities to learn, reason, work autonomously and be totally dynamic. The ultimate goal of Multi-Agent based DLE technology is to develop human-like intelligent agents that will be able to reason and learn through their experiences by applying techniques from computational intelligence field, namely, reasoning techniques such as case-based reasoning and rule-based reasoning and machine learning techniques such as neural networks and genetic algorithms. They should also be able to handle uncertain and fuzzy knowledge in order to be able to behave correctly in real world distributed environments. To model the knowledge of specific domains, ontologies can be used. Their main contribution is the efficient support to integrate information coming from different sources to model educational domains and to build specific learning resources. An advanced ontology management system for personalized e-learning is presented in [17]. The agent technology could also be used to built distributed Intelligent Tutoring Systems (ITS) and distributed LMS following architectures containing repositories for user profiles, devices’ profiles and learning objects, course databases and multi-agents systems composed by stationary and mobile agents. A systems resulting from the implementation of these ideas is ISABEL [18]. The main idea in ISABEL is to partition the students in clusters of students having similar profiles, where each cluster is managed by a tutor agent. In [19] an application of intelligent techniques and semantic web technologies is discussed and a domain ontology as well as a knowledge based multi-agent system
lying on these technologies are developed, allowing to automatically control students’ acquired knowledge in elearning frameworks. MAGADI framework [20] results from merging characteristics of three current technologies: adaptive intelligent systems, authoring tools and LMS technologies. Its main aim is to be the technological component of a blended learning environment. As a result it allows for the flow of information and synergies between on-line and offline environments. Although web technologies and AI techniques have been used successfully in a number of ALE, they have not yet been adopted in widely used e-learning systems, namely the open-source LMS. V.
ADAPTIVE COURSES IN LEARNING MANAGEMENT SYSTEMS Educational technology is moving towards a new wave of standard-based VLE which, at current stage, provide only little or no adaptivity to their users. LMS, either commercial such as Blackboard and WebCT, or free opensource such as Moodle and Claroline, are very widely and successfully used in e-learning environments but hardly offer any information about which instructional methods and pedagogical models they use. The only known standard-based modern VLE that try to encompass some adaptive characteristics are Dokeos and Moodle [21] Dokeos (http://www.dokeos.com) is a quite complex open source e-learning source online learning suite, evolved out of the free LMS Claroline. Most parts of the software can be downloaded for free, whereas others are offered on a commercial basis by the like-named company. In terms of adaptivity Dokeos provides progress-based learning paths as teachers may define prerequisites for content elements. Moodle (http://moodle.com/) is one of the most popular free open source LMS. The general design tries to consider pedagogical principles and learning theories. The lesson module of Moodle also provides different learning paths. As the student’s possible answers on a question can be used as starting points for selecting different learning paths, some kind of “weak adaptivity” is supported although the suite itself supports no student model. Although adaptivity has been a research topic for about fifteen years, it was studied and implemented mainly in research projects rather than in the LMS that are used in practice. This situation is changing, as recent research on e-learning focuses on the enhancement of the popular LMS systems with “strong” adaptivity. An adaptive system has as its primary goal the reformulation of the content and services in order to meet the needs of different users. When the adaptive system is a largely used open source LMS, it has also to be easy to use and to be further maintained and well supported and must provide the capacity to add new functionality without changes in the existing source code [21]. Minimum adaptivity in LMS usually includes collecting data about the student in order to decide about his/her learning style [22]. This type of adaptation takes under
consideration the specific learner’s learning preferences which characterize the learner as an active, reflective, sensing, intuitive, sequential or global learner. Adaptivity based on learning styles focuses on specific course elements and refers to the sequence and the number of presented learning elements. The adaptation features typically include the sequence of content objects like examples, exercises, and self-assessment tests and determine where these should be presented in relation to the course chapters. The alternative sequences of content objects are imprinted in the learning paths of the LMS. In order the LMS to be considered as offering “strong” adaptivity, it must be able to guide each particular learner towards the most suitable learning path and dynamically adapt to the learner’s progress. More often, however, learners reply to questionnaires when they first enter a course and from their answers the student model is designed and taken under consideration for the rest of their interaction with the system. Graf and Kinshuk [22] address the limitations of current e-learning platforms, by designing a meta-model for adaptive courses which can be easily integrated into elearning platforms to foster adaptivity. The fundamental concept of this meta-model is to create a group of individual leaning objects and then compose them to form individual courses according to each learner’s preferences. The meta-model has been implemented as an add-on into the open source LMS Moodle and is capable of generating different course instances. This represents a static way of adaptation. In order to demonstrate the positive effects of their adaptive learning environment in supporting students in learning they have also conducted an experiment with promising results. Boticario et al. [23] have developed a standard-based LMS called aLFanet, whose main feature is to provide adaptive course delivery based on an extensive use of standards and several user modeling techniques, included in a multi-agent architecture. Their focus is to effectively combine runtime and design time adaptations in an open adaptive LMS based on standards. Thus, apart from using a learning design based on a standards (IMS-LD, in this case) to cover the dynamic behavior of the system, they provide an adaptive runtime environment which extends learning design adaptations, integrates other educational standards and gives access to control the corresponding feedbacks between design decisions and runtime interactions supported by user modeling. Their current research is on how to extend those models to cope with accessibility and functional diversity issues to provide services for “all”, which take into account pedagogical and psychological issues9. Oneto and colleagues [24] have tried to integrate popular Learning Management Systems like Moodle, Sakai, Claroline, learn eXact and CLIX into an Adaptive Learning Environment called GRAPPLE which is in charge of adapting and then personalizing contents based on the user pre-knowledge, prerequisites and preferences. GRAPPLE collects all the information related to the users 9
http://www.eu4all-project.eu/
and their activities and uses a) its Adaptive Learning Engine to deploy adaptive courses, b) its User Modeling Framework to manage the user model data, and c) an authoring tool that allows creating courses. Prodromou and Avouris [25] have presented e-Class Personalized (e-CP), a new extension of the widely available open source Learning Management System eClass10, spawned from the open-source LMS Claroline. eCP monitors interaction of the users of e-Class with its content and services and adapts the services to better match the users’ interests and tasks. The Adaptive Subsystem of the e-CP was designed and developed as a module of e-Class, to meet these requirements. In effect, this subsystem takes over the control from the central system of e-class when a user logs in and adapts the user interface according to the specific characteristics of the user, found in the user model. The most prominent characteristic of this module is to provide all relevant and timely information grouped in the same page, using a ranking mechanism to allow for the most relevant information to appear at the top of the list, taking in consideration historical data of interaction. e-CP has been tested with positive response by the user population that were exposed to this combination of the LMS and its adaptation services. Chaoui, and Laskri [26] have developed different tools in their approach to create Educational Adaptive Web Content in the open source LMS openelms11. They have used a search and filtering tool to find for their ontology all the needed content from the existing web free resources, using the Google API of search. Their course is created in an automatic manner via the tool for adaptation of content and from different bricks / segments stored in the database segments. Profiles of learners are also presented in another pedagogical ontology, to give a direct adaptation via the current state of the learner by making a test or examination. Adaptation of segments to learners is based on pedagogical ontology and patterns principals. CONCLUSIONS In VLS we can identify two groups of systems. The first group contains all those systems like LMS, CMS, CrMS and LCMS which support a lot of standard features that any learning platform is expected to include, but they do not provide adaptivity. The systems of the second group, AIWBES, provide these adaptive and intelligent features but as many of the “standard features” are missing, they are rather not suitable for common e-learning scenarios. According to our review presented in previous sections we can conclude that over the past two decades a lot of effort has been put into the field of adaptive VLE. There exist a considerable number of AIWBES which already encompass adaptive characteristics and some of them show some intelligence. Unfortunately, none of these systems is widely used outside the educational research area.
10 11
http://eclass.gunet.gr http://www.openelms.org/default.htm
At the other hand, most of the popular LMS have not yet taken advantage of intelligent and adaptive technologies although some adaptive web-services and intelligent plugins start to be offered to the worldwide educational community. Nowadays, research projects are aiming at making AIWBES available to a large number of learners like popular LMS do, combining features from both groups of systems and providing evidence regarding the efficiency of their adaptivity. Another research tendency is to transfer the achievements in AIWEBS into the most frequently used LMS designing web-services and plug-ins or other supportive software that will bring adaptivity and intelligence into them. Minimum adaptivity needs the design and maintenance of the student model for each individual student. Most of current adaptive LMS permit a static way of adaptation. Future research should focuses on dynamic aspects of adaptation, detect the learner’s characteristics from their behavior during the course and use this information to assign a suitable course instance. The technological potential alone, as discussed throughout the paper, is useless if the pedagogical use of elearning systems is ignored. Therefore, the design and development of VLE should also pay attention to the teaching and learning processes, and on how to support and facilitate them. REFERENCES [1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
M. Tallent-Runnels, J. Thomas, W. Lan, W. Cooper, S. T. Ahern and S. Shaw, “Teaching courses online: A review of the research,” Review of Educational Research”, vol. 76(1), pp. 93135, 2006. S. Franklin, and M. Peat, "Managing Change: The Use of Mixed Delivery Modes to Increase Learning Opportunities," Australian Journal of Education Technology, vol. 17, pp. 37-49, 2001. W. Härdle, S. Klinke, and U. Ziegenhagen, "e-Learning Statistics – A Selective Review," Commission on Behavioral and Social Sciences and Education, How People Learn: Brain, Mind, Experience and School: Expanded Edition, ch8., pp. 230, 2006. P.Brusilovsky and C. Peylo, “Adaptive and intelligent Web-based educational systems.” In P. Brusilovsky and C. Peylo (eds.) ,International Journal of Artificial Intelligence in Education, vol.13(2-4), Special Issue on Adaptive and Intelligent Web-based Educational Systems, pp. 159-172, 2003. I. Hatzilygeroudis, C. Koutsojanni and N. Papachristou, “Improving The Adaptiveness of an e-Learning System,” in M. Wallace, MC Angelides and P. Mylonas (Eds.), “Advances In Semantic Media Adaptation and Personalisation," Studies In Computational Intelligence, Springer-Verlag, pp. 177-198, 2008. W. Dunwei and L. Fuhua, “Ways and Means of Employing AI Technology in E-Learning Systems,” in proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies, pp.1005-1006, 2008. G. Weber, H-C. Kuhl and S. Weibelzahl, “Developing adaptive internet based courses with the authoring system NetCoach,” in S. Reich, M. M. Tzagarakis, and Paul de Bra, editors, Hypermedia: Openness, Structural Awareness, and Adaptivity, Lecture Notes in Computer Science, vol. 2266, pp. 222-223, 2002. P. De Bra, N. Stash, D. Smits, C. Romero and S. Ventura, “Authoring and Management Tools for Adaptive Educational Hypermedia Systems: The AHA! Case Study,” iIn: Studies in Computational Intelligence (SCI), vol. 62, ,pp. 285-308, 2007. P. Brusilovsky, J. Eklund and E. Schwarz, “Web-based education for all: A tool for developing adaptive courseware. Computer
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
Networks and ISDN Systems”. Proceedings of Seventh International World Wide Web Conference, vol. 30 (1-7), pp. 291300, 1998. O. Conlan and V. Wade, “Evaluation of APeLS - an adaptive eLearning service based on the multi-model, metadata-driven approach, Lecture Notes in Computer Science,” in proceedings of the Third International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH2004), Eindhoven, The Netherlands, August 2004, edited by De Bra, P., Nejdl, W. , 3137, Springer, pp 291 – 295, 2004. T. Heift and D. Nicholson, “Web delivery of adaptive and interactive language tutoring,” International Journal of Artificial Intelligence in Education, vol. 12(4), pp. 310-324, 2001. A. Mitrovic, “An Intelligent SQL Tutor on the Web,” International Journal of Artificial Intelligence in Education, vol. 13(2-4), pp. 171195, 2003. G. Weber and P. Brusilovsky, “ELM-ART: An adaptive versatile system for Web-based instruction,” International Journal of Artificial Intelligence in Education, vol. 12(4), pp. 351-384, 2001. E. Popescu, C, Bădică and L. Moraret, “WELSA: An Intelligent and Adaptive Web-Based Educational System,” Studies in Computational Intelligence, vol. 237, pp. 175-185, 2009. B. Giloglugil and M.M. Inceoglu, “Exploring the State of the Art in Adaptive Distributed Learning Environments,” in D. Taniar et al. (Eds.): ICCSA 2010, Part II, LNCS 6017, pp. 556–569, 2010. V. Devedžić, “Web Intelligence and AIED,” in proceedings of the AIED 2003 Workshop Towards Intelligent Learning Management Systems, 2003, Sydney, pp. 23-33. M. Gaeta, F. Orciuoli and P. Ritrovato, “Advanced ontology management system for personalised e-Learning,” KnowledgeBased Systems. 22(4), pp. 292–301, 2009. S. Garruzzo, D. Rosaci and G.M.L. Sarne, “ISABEL: A Multi Agent e-Learning System That Supports Multiple Devices,” in: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 85–88, 2007. A. Gladun, J. Rogushina, F. Garcıa-Sanchez, R. Martínez-Béjar and J.T. Fernández-Breis, “An application of intelligent techniques and semantic web technologies in e-learning environments,” Expert Systems Applications, vol. 36(2), pp. 1922–1931, 2009. A. Alvarez, S. Ruiz, M. Martín, I. Fernández-Castro and M. Urretavizcaya, “MAGADI: a Blended-Learning Framework for Overall Learning,” Artificial Intelligence in Education, pp. 557-564, 2009. D. Hauger and M. Kock, “State of the Art of Adaptivity in ELearning Platforms,” Workshop at Adaptivity and User Modeling”, in Interactive Systems ABIS 2007, Halle/Salle, Germany, 2007. S. Graf and Kinshuk, “Providing Adaptive Courses in Learning Management Systems with Respect to Learning Styles,” in T. Bastiaens & S. Carliner (Eds.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2007 , pp. 2576-2583, 2007 . J. Boticario and O. Santos, “Issues in Developing Adaptive Learning Management Systems for Higher Education Institutions,” International Workshop on Adaptive Learning and Learning Design,Adaptive Hypermedia, 2007. L. Oneto, F. Abel, E. Herder and D. Smits, “Making today's Learning. Management Systems adaptive, ” EC-TEL'2009, Worshop 8, 2009. E.G. Prodromou, and N. Avouris, “e-Class Personalized: Design and Evaluation of an Adaptive Learning Content Management System, ” in Artificial Intelligence Applications and Innovations Springer Boston , vol 204, pp 409-416. M. Chaoui and M-T. Laskri, “Towards the Creation of Adaptive Content from Web Resources in an E-Learning Platform to Learners Profiles,” World Academy of Science, Engineering and Technology, vol 77, pp157-162, 2011.
AUTHORS K. Georgouli is with the Technological Educational Institute of Athens, Agiou Spiridonos, 12210 Egaleo, Greece (e-mail: kgeor@teiath.gr).